Arc Filtering Method Based on Generative Adversarial Networks

Robot monitoring has been widely applied to observe the operation of welding robots, but the arc generated during the welding process causes severe overexposure in local areas of robot surveillance images, significantly deteriorating the monitoring effect of the welding robots. Due to the complex background structure of robot welding images, existing methods produce poor quality images after filtering out the arc from robot welding images. In this study, we propose an arc filtering model based on generative adversarial networks (GANs), primarily aimed at filtering out the arc in robot welding images. The well-trained arc filtering model can effectively eliminate the arc in robot welding images, generating high-quality non-arcing images. Experimental studies were conducted using an industrial robot welding image dataset we built autonomously and compared with state-of-the-art methods, demonstrating the superiority of our proposed model.